# python3 scripts\run_rob_mots.py --ROBMOTS_SPLIT val --TRACKERS_TO_EVAL tracker_name (e.g. STP) --USE_PARALLEL True --NUM_PARALLEL_CORES 4

import sys
import os
import csv
import numpy as np
from multiprocessing import freeze_support

import trackeval  # noqa: E402
from trackeval import utils

sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))

code_path = utils.get_code_path()

if __name__ == "__main__":
    freeze_support()

    script_config = {
        "ROBMOTS_SPLIT": "train",  # 'train',  # valid: 'train', 'val', 'test', 'test_live', 'test_post', 'test_all'
        "BENCHMARKS": [
            "kitti_mots",
            "davis_unsupervised",
            "youtube_vis",
            "ovis",
            "tao",
        ],  # 'bdd_mots' coming soon
        "GT_FOLDER": os.path.join(code_path, "data/gt/rob_mots"),
        "TRACKERS_FOLDER": os.path.join(code_path, "data/trackers/rob_mots"),
    }

    default_eval_config = trackeval.Evaluator.get_default_eval_config()
    default_eval_config["PRINT_ONLY_COMBINED"] = True
    default_eval_config["DISPLAY_LESS_PROGRESS"] = True
    default_dataset_config = trackeval.datasets.RobMOTS.get_default_dataset_config()
    config = {**default_eval_config, **default_dataset_config, **script_config}

    # Command line interface:
    config = utils.update_config(config)

    if config["ROBMOTS_SPLIT"] == "val":
        config["BENCHMARKS"] = [
            "kitti_mots",
            "bdd_mots",
            "davis_unsupervised",
            "youtube_vis",
            "ovis",
            "tao",
            "mots_challenge",
        ]
        config["SPLIT_TO_EVAL"] = "val"
    elif config["ROBMOTS_SPLIT"] == "test" or config["SPLIT_TO_EVAL"] == "test_live":
        config["BENCHMARKS"] = [
            "kitti_mots",
            "bdd_mots",
            "davis_unsupervised",
            "youtube_vis",
            "ovis",
            "tao",
        ]
        config["SPLIT_TO_EVAL"] = "test"
    elif config["ROBMOTS_SPLIT"] == "test_post":
        config["BENCHMARKS"] = ["mots_challenge", "waymo"]
        config["SPLIT_TO_EVAL"] = "test"
    elif config["ROBMOTS_SPLIT"] == "test_all":
        config["BENCHMARKS"] = [
            "kitti_mots",
            "bdd_mots",
            "davis_unsupervised",
            "youtube_vis",
            "ovis",
            "tao",
            "mots_challenge",
            "waymo",
        ]
        config["SPLIT_TO_EVAL"] = "test"
    elif config["ROBMOTS_SPLIT"] == "train":
        config["BENCHMARKS"] = [
            "kitti_mots",
            "davis_unsupervised",
            "youtube_vis",
            "ovis",
            "tao",
        ]  # 'bdd_mots' coming soon
        config["SPLIT_TO_EVAL"] = "train"

    metrics_config = {"METRICS": ["HOTA"]}
    # metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity']}
    eval_config = {k: v for k, v in config.items() if k in config.keys()}
    dataset_config = {k: v for k, v in config.items() if k in config.keys()}

    # Run code
    dataset_list = []
    for bench in config["BENCHMARKS"]:
        dataset_config["SUB_BENCHMARK"] = bench
        dataset_list.append(trackeval.datasets.RobMOTS(dataset_config))
    evaluator = trackeval.Evaluator(eval_config)
    metrics_list = []
    for metric in [
        trackeval.metrics.HOTA,
        trackeval.metrics.CLEAR,
        trackeval.metrics.Identity,
    ]:
        if metric.get_name() in metrics_config["METRICS"]:
            metrics_list.append(metric())
    if len(metrics_list) == 0:
        raise Exception("No metrics selected for evaluation")
    output_res, output_msg = evaluator.evaluate(dataset_list, metrics_list)

    # For each benchmark, combine the 'all' score with the 'cls_averaged' using geometric mean.
    metrics_to_calc = [
        "HOTA",
        "DetA",
        "AssA",
        "DetRe",
        "DetPr",
        "AssRe",
        "AssPr",
        "LocA",
    ]
    trackers = list(output_res["RobMOTS." + config["BENCHMARKS"][0]].keys())
    for tracker in trackers:
        # final_results[benchmark][result_type][metric]
        final_results = {}
        res = {
            bench: output_res["RobMOTS." + bench][tracker]["COMBINED_SEQ"]
            for bench in config["BENCHMARKS"]
        }
        for bench in config["BENCHMARKS"]:
            final_results[bench] = {"cls_av": {}, "det_av": {}, "final": {}}
            for metric in metrics_to_calc:
                final_results[bench]["cls_av"][metric] = np.mean(
                    res[bench]["cls_comb_cls_av"]["HOTA"][metric]
                )
                final_results[bench]["det_av"][metric] = np.mean(
                    res[bench]["all"]["HOTA"][metric]
                )
                final_results[bench]["final"][metric] = np.sqrt(
                    final_results[bench]["cls_av"][metric]
                    * final_results[bench]["det_av"][metric]
                )

        # Take the arithmetic mean over all the benchmarks
        final_results["overall"] = {"cls_av": {}, "det_av": {}, "final": {}}
        for metric in metrics_to_calc:
            final_results["overall"]["cls_av"][metric] = np.mean(
                [
                    final_results[bench]["cls_av"][metric]
                    for bench in config["BENCHMARKS"]
                ]
            )
            final_results["overall"]["det_av"][metric] = np.mean(
                [
                    final_results[bench]["det_av"][metric]
                    for bench in config["BENCHMARKS"]
                ]
            )
            final_results["overall"]["final"][metric] = np.mean(
                [
                    final_results[bench]["final"][metric]
                    for bench in config["BENCHMARKS"]
                ]
            )

        # Save out result
        headers = [config["SPLIT_TO_EVAL"]] + [
            x + "___" + metric for x in ["f", "c", "d"] for metric in metrics_to_calc
        ]

        def rowify(d):
            return [
                d[x][metric]
                for x in ["final", "cls_av", "det_av"]
                for metric in metrics_to_calc
            ]

        out_file = os.path.join(
            script_config["TRACKERS_FOLDER"],
            script_config["ROBMOTS_SPLIT"],
            tracker,
            "final_results.csv",
        )

        with open(out_file, "w", newline="") as f:
            writer = csv.writer(f, delimiter=",")
            writer.writerow(headers)
            writer.writerow(["overall"] + rowify(final_results["overall"]))
            for bench in config["BENCHMARKS"]:
                if bench == "overall":
                    continue
                writer.writerow([bench] + rowify(final_results[bench]))
